Decision support systems (DSS) are computer-based information systems for supporting decision making activities. They are widely used in various medical contexts, e.g. in biomedical research and diagnostics and in particular in the health care sector where DSSs are used for automatically determining the current health status of a patient given a set of input parameter values and/or for predicting medical conditions.
Various decision support systems exist which differ regarding the input parameters and/or the algorithmic approach used for calculating a decision, regarding the software architecture and regarding the output returned by a DSS. A DSS may assist a medical practitioner, e.g. a physician in a hospital or in a medical practice, in taking a decision by providing probability values for one or more possible diagnoses and/or or may return a comprehensive diagnosis including suggestions for surgical treatment and medication.
US 2008/0263050 describes a computer-implemented method for managing data for a clinical decision support system. The decision support system includes a plurality of rules.
The medical information decision support system disclosed in US 2002/0091687 A1 is also based on the usage of rules. According to said system, a decision generator determines options for providing medical service to patients based on information received from an information/directives repository, an adaptive chart and input from a user. Said information/directives repository comprises e.g. clinical practice guidelines, formulary statements, algorithms, protocols, care-maps and differential diagnosis trees.
Rule-based DSS, but also DSS based on other algorithms such as decision trees, Bayesian networks, clustering and other machine learning techniques commonly face the problem that they are highly specialized on a small set of possible diagnoses. They are expert systems for a highly specialized field, used by a small group of specialists and are not suited for usage e.g. in a family doctor's practice or a hospital where a multitude of different diseases are treated.
Medical DSSs with a more general scope are hampered by the fact that they require a multitude of patient related data in order to perform a prediction. Often, it is not possible to obtain this data: in an emergency case, there may not be enough time for obtaining the totality of data required by a DSS. A family doctor's practice may not comprise all devices necessary to obtain the multitude of data values required as input for medical DSS with a general predictive scope.
A further problem associated with the large number of biomedical parameters current DSSs require as input is, that the computational costs of predicting a medical condition by a DSS often grow at a non-linear, e.g. exponential, pace with a growing number of input parameters. Accordingly, DSSs with a general scope requiring a multitude of input parameters to ensure an acceptable accuracy level and coverage of the prediction tend to be slow and computationally expensive. Accordingly, working with such DSSs tends to be slow and can be highly uncomfortable, especially if the DSS is installed on hardware with limited computational power, e.g. old computers, netbooks, mobile phones and other mobile devices.
The objective of embodiments of the present invention is to provide for an improved decision support system, in particular an improved remote decision support system.